ENHANCING RETRIEVAL-AUGMENTED GENERATION (RAG) SYSTEMS FOR ACCURATE AND HALLUCINATION-FREE AI RESPONSES

Abstract

With the rapid development of Artificial Intelligence (AI) and Large Language Models (LLMs), the ways in which knowledge is created, knowledge support decisions and human–computer interaction have also undergone a transformation in many fields such as health care, education, finance, governance and scientific research. While these are promising developments, the broad roll out of generative AI systems has been marred by the continuing problem of AI hallucinations, where models offer factually incorrect, misleading or unverifiable information. These restrictions are a major concern with regard to the trustworthiness, reliability and transparency of AI technologies and their responsible use in critical environments. To address these challenges, Retrieval-Augmented Generation (RAG) was proposed as a promising new architectural approach to improve the performance of LMs.

This qualitative study investigates how Retrieval-Augmented Generation systems can decrease hallucinations and improve the consistency of AI responses. This is qualitative interpretive research, based on expert interviews, semi-structured interviews, analysis of industry documents, and case-based investigations of current RAG implementations. The research analyzes key problems on the etiology of hallucinations in AI, retrieval quality and accuracy of responses, methods for grounding knowledge, explainability for users, organizational problems with the uptake of AI, and other ethical and governance issues. The results indicate that successful retrieval, the quality of the retrieved information, and a high level of integration of the retrieved information into the context are important factors in reducing hallucinations and enhancing the credibility of the response. Key factors impacting on trust and successful organizational adoption are identified, including governance structures, explainability and transparency. The research can be theoretically applied in the fields of artificial intelligence, information retrieval and reliable AI, and can provide a complete qualitative understanding of the role of retrieval-augmented architectures in tackling the fundamental limitations of generative models. The findings offer valuable insights to AI developers, technology companies, researchers and policymakers looking to develop and design more trustworthy and responsible AI applications. This lack of hallucinations is an important step toward building reliable and human-friendly intelligent systems. The study shows that Retrieval-Augmented Generation is an important step toward more trustworthy and reliable AI-generated responses.

Keywords : Retrieval-Augmented Generation (RAG), Artificial Intelligence, Large Language Models, AI Hallucinations, Knowledge Retrieval, Explainable AI, Trustworthy AI, Qualitative Research, topics discussed.